Prof. Panos K. Chrysanthis was a keynote speaker at the 2016 Joint Conference on Medical Informatics (JCMIT) in Taiwan on June 11, 2016 and briefly spoke at the graduation ceremony of the Graduate Institute of Biomedical Informatics at the Taiwan Medical University on June 13, 2016.

The initiative invites distinguished Professors/Scientists from abroad to carry out research at the leading University of Cyprus. As part of this initiative, Prof. Chrysanthis gave a talk titled "Recommending Interesting Visualizations for Data Exploration" on June 1, 2016 and delivered a tutorial on "Graph Partitioning in Distributed Graph Computation" on June 2, 2016.

Abstract: The proliferation of mobile, ubiquitous and spatial computing has led to a number of services aiming into facilitate the exploration of a city. Platforms such as Foursquare and Yelp curate information about establishments in an area that can then be used for recommendation purposes. Traditionally an approach followed by these systems is to rank places based on their popularity, proximity or any other feature that represents the quality of the venue and then return the top-k of them. However, this approach, while simple and intuitive, is not necessarily providing a diverse set of recommendations, since similar venues typically are ranked closely. Therefore, in this paper we design and introduce MPG (which stands for Mobile Personal Guide), a mobile service that provides a set of diverse venue recommendations better aligned with user preferences. MPG takes into consideration the user preferences (e.g., distance willing to cover, types of venues interested in exploring, etc.), the popularity of the establishments, as well as their distance from the current location of the user by combining them in a single composite score. We evaluate our approach using a large- scale dataset of approximately 14 million venues collected from Foursquare. Our results indicate that MPG can increase coverage of the result set compared to the baselines considered. It further achieves a significantly better Relevancy-Diversity trade-off ratio.